An efficient branch and bound method for face recognition

Recently, researchers have proposed many face recognition methods with the aim of improving the accuracy rate of face recognition. However, few face recognition methods focus on computational cost. To reduce the computational cost of face recognition, we propose an effective face recognition method using Haar wavelet features and a branch and bound method. Our proposed method extracts features of the Haar wavelet from a normalized face image, and recognizes the face by classifiers learned with the AdaBoost M1 algorithm. To increase the efficiency of the recognition process, we select features according to the accuracy of classification and apply a branch and bound method to the recognition tree into which the classifiers of an individual in the face database are merged. Experimental results show that our proposed method reduces the calculated classifiers in the recognition tree by 72.1% and achieves an overall reduction in the computational cost.